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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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加强医学成像教育:整合计算技术,数字图像处理和人工智能.

Sibusiso Mdletshe1, Alan Wang2,3,4,5

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这篇论文提出了一个更新的医学成像 (MI) 课程,整合计算,数字图像处理和人工智能 (AI). 目标是为未来的MI专业人员提供必要的技能,以提高诊断准确性和工作流程效率.

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科学领域:

  • 医学成像教育教育 医学成像教育
  • 医疗保健中的计算技术
  • 人工智能应用程序 人工智能应用程序

背景情况:

  • 技术进步正在改变医学成像 (MI).
  • 目前的本科MI课程可能缺乏必要的计算和AI组件.
  • 需要为MI专业人员为不断变化的行业需求做好准备.

研究的目的:

  • 探索计算机技术,数字图像处理和AI在本科MI教育中的整合.
  • 确定当前MI教育实践中的差距和局限性.
  • 为准备好未来的MI专业人士提出一个全面的课程框架.

主要方法:

  • 审查当前的医学成像教育实践.
  • 分析现有的课程差距和局限性.
  • 拟议的课程框架的开发,包括计算技能,图像处理和AI工具.

主要成果:

  • 确定MI教育课程增强的关键领域.
  • 建议一个整合Python,MATLAB,高级图像处理和AI (例如,ChatGPT) 的框架.
  • 该框架旨在弥合学术培训和专业实践之间的差距.

结论:

  • 整合计算技能,先进的图像处理和AI对于现代MI教育至关重要.
  • 拟议的课程框架可以显著提高MI教育质量.
  • 这种方法将使学生更好地应对未来的挑战,提高诊断准确性和工作流程效率.